Open AccessSimulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets
Author(s) -
Longqing Li
Publication year - 2018
Publication title -
applied economics and finance
Language(s) - English
Resource type - Journals
eISSN - 2332-7308
pISSN - 2332-7294
DOI - 10.11114/aef.v5i4.3376
Subject(s) - cvar , portfolio optimization , portfolio , diversification (marketing strategy) , econometrics , risk measure , expected shortfall , economics , spectral risk measure , quantile , value at risk , project portfolio management , modern portfolio theory , efficient frontier , robustness (evolution) , selection (genetic algorithm) , monte carlo method , risk management , actuarial science , computer science , mathematics , statistics , financial economics , business , finance , artificial intelligence , chemistry , biochemistry , management , marketing , project management , gene
Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.